Update app.py

app.py

@@ -27,10 +27,10 @@ def predict_employee_status(
     work_accident, promotion_last_5years, salary, department, threshold=0.5
 ):
     departments = [
-        'sales', 'accounting', 'hr', 'technical', 'support',
-        'management', 'IT', 'product_mng', 'marketing', 'RandD'
+        'IT', 'RandD', 'accounting', 'hr', 'management',
+        'marketing', 'product_mng', 'sales', 'support', 'technical'
     ]
-    
+
     # One-hot encode department
     department_features = {f"department_{dept}": 0 for dept in departments}
     if department in departments:
@@ -57,7 +57,7 @@ def predict_employee_status(
 
     input_df = pd.DataFrame(input_data)
 
-    # Match training column order exactly
+    # Ensure exact column order
     expected_columns = [
         'satisfaction_level', 'last_evaluation', 'number_project', 'average_monthly_hours',
         'time_spend_company', 'Work_accident', 'promotion_last_5years', 'salary',
@@ -66,13 +66,11 @@ def predict_employee_status(
         'department_management', 'department_marketing', 'department_product_mng',
         'department_sales', 'department_support', 'department_technical'
     ]
-
     for col in expected_columns:
         if col not in input_df.columns:
             input_df[col] = 0
     input_df = input_df[expected_columns]
 
-    # Predict
     try:
         prob = model.predict_proba(input_df)[0][1]
         result = "✅ Employee is likely to quit." if prob >= threshold else "✅ Employee is likely to stay."